Plant breeding is the art and science of increasing a plant's value through genetic manipulation. The plant breeder intermates plants with different genetic backgrounds an, attempts to identify and select progeny with superior genetic composition and hence superior phenotypic performance. A difficulty for the plant breeder is accurately determining what the best genotype is. He or she must rely on phenotypic measurements to understand the genotype.
Many traits of importance, like grain yield, are measured quantitatively. These quantitatve traits typically display non-discrete phenotypic distributions which are the result of many genetic and environmental factors. A direct consequence is that the phenotype is frequently a weak predictor of the genotype. Thus, the selection of superior genotypes can be a challenging endeavor.
Trait selection based upon genetic markers has been suggested as a more direct method of selecting superior genotypes. In order for genetic marker-based selection to be successful however, an association between marker loci and trait loci must first be established. The resolution of quantitative traits into discrete genetic factors is the first step in this process.
Numerous examples exist of the genetic dissection of quantitative traits with genetic markers (Stuber, C. W. 1992. Plant Breed. Rev 9:37-61). These and other studies attempt to identify the location of quantitative trait loci (QTLs) in relation to linked marker loci. The discovery of these genetic linkage relationships is key if markers are to be successfully used to select for linked QTLs. Once this association is established, selection based upon marker genotypes is facilitated.
There have been four general methods used to identify marker loci in linkage with and predictive of a quantitative trait. Two of these methods measure changes in marker allele frequency in response to selection. Stuber et al. (Stuber, C. W. et al., 1980. Genetics 95:225-236) is exemplary of one of these methods of analysis. Within a given population, marker allele frequencies are measured before and after selection. Significant changes in allele frequency at a marker locus is presumed to be due to linkage between marker locus and selected trait locus.
U.S. Pat. No. 5,437,697 also describes allele frequency changes as a means for identification of predictive marker loci. The marker allele frequency of elite lines is compared with the allele frequency of their progenitors. Marker loci in linkage with QTLs are identified by non-random changes in marker allele frequency among the elite lines examined. These non-random marker allele frequency changes are presumed to be due to phenotypic selection of trait loci linked to marker loci.
A third method for identifying marker-QTL relationships analyzes segregating populations derived from the intermating of two parent lines (Edwards, M. D. et al., 1987. Genetics 116:113-125; Nienhuis, J. et al., 1987. Crop Sci. 27:797-803). The parent lines are typically selected for their phenotypic and genotypic incongruence. These studies attempt to take advantage of the high degree of genetic disequilibrium present in F2, backcross and to a lesser degree recombinant inbred populations. By minimizing opportunities for recombination, marker loci need not be tightly linked to QTLs in order to establish a significant association. Marker loci linked to QTLs are identified by making locus-by-locus comparisons of the mean phenotypic performance of marker allele classes. It is assumed that marker loci with unequal phenotypic means are in linkage with one or more QTLs.
An alternative to single marker analysis was proposed by Lander and Botstein (E. S. Lander and D. Botstein, 1989. Genetics 121:185-199) and later used to map QTLs in a plant species (Paterson, A. H. et al., 1988. Nature 335:721-726.). The method, known as interval mapping, estimates the statistical likelihood of a QTL being located at pre-defined intervals between marker loci. Both single marker analysis and interval mapping use the same types of experimental populations described earlier.
All marker-based mapping methods rely on accurate determination of the phenotype. For traits with high heritability this is not a problem, but for most traits of agronomic interest, especially yield, the ability to measure the trait accurately is difficult because these traits exhibit low heritability. The heritability of a trait is defined in the broad sense as the ratio of the genetic variance to the total phenotypic variance. Many agronomic traits display low heritability; i.e., the performance of parent plants is a poor predictor of offspring performance. Thus, traits with low heritability have small genetic variance components in comparison with observed phenotypic variation. The impact on the plant breeder is that in breeding populations, the value of a plant's genetic composition is difficult to determine from agronomic trait measurements. In an attempt to maximize their discriminative abilities, breeders collect multiple measurements both from individuals related by descent and from many environments. This strategy is resource intensive because it involves the use of extensive trialing to make even small gains in plant improvement.
To improve measurements of yield and other traits with low heritability, replicated progeny and multiple environments have been evaluated (Stuber, C. W. et al., 1992. Genetics 132:823-839). Unfortunately, a truly accurate assessment of phenotype requires far greater replication across many spatial and temporal environments.
A more serious drawback exists with studies using experimental populations. These studies are limited in context in that a maximum of only two alleles are segregating. Accordingly, analyses can only compare the effect of one allele against the second. If these alleles do not exhibit sufficient phenotypic incongruence, then the QTL is not identified. In reality there are likely many other alleles, some with positive phenotypic effects, within the species which, if identified, could be exploited by the plant breeder.
Pedigree-based analysis as disclosed in U.S. Pat. No. 5,437,697 attempts to overcome the shortcomings of earlier methods. A difficulty with this and other allele frequency based approaches is the dependence upon phenotypic selection as the driving force for allele frequency changes. Alleles with strong phenotypic effects will consistently be selected in segregating breeding populations. These QTLs will readily be identified using allele frequency-based approaches. However, loci with alleles with only subtle phenotypic effects will likely only be selected occasionally. Many QTLs containing these potentially desirable alleles will therefore not be detected.
A further handicap when using the method disclosed in U.S. Pat. No. 5,437,697 is the ability to only detect associations with trait loci for overall agronomic fitness. Plant breeders select for a plurality of traits simultaneously, and chosen individuals are represented by their composite performance. Depending upon the emphasis, new varieties could embody the improvement of a specific phenotypic weakness (e.g., disease resistance or a general improvement in yield). The method is thus fully dependent upon the whims of plant breeding to alter allele frequencies. The ability to detect loci associated with specific phenotypic traits is impaired.
A result of the various drawbacks to previous methods of identifying significant marker-QTL associations is that relatively few QTLs are identified for complex quantitative traits like yield, and inconsistencies in marker-QTL associations are found. The experiments of Stromberg et al., (Stromberg, L. D. et al., 1994. Crop Sci. 34:1221-1225) are particularly illustrative of these difficulties. In their study, first eight and later ten QTLs were identified for yield, in all likelihood a fraction of the true number of QTLs affecting yield. Despite re-mapping in lines derived directly from the original mapping population, only one marker-QTL association was in common between the early and later generation test.
Once marker-QTL relationships are established, marker loci are used as predictors of the trait(s) of interest. This predictive information is used in two ways. First, using genotypic germplasm survey data, parental lines may be chosen for their favorable genotypic composition. Second, segregating progeny in breeding populations may be selected based upon their similarity to the genotype predicted to have the best phenotypic performance (Stomberg, L. D. et al., 1994. Crop Sci. 34:1221-1225).
An alternative use for marker information as a yield predictor is as an estimator of genetic incongruence. Using a germplasm survey of parental lines, Bernardo (Bernardo, R. 1994. Crop Sci. 34:20-25) used restriction fragment length polymorphisms to estimate co-ancestry. This information, along with yield information, was used to predict test cross yields in maize. A similar analysis using marker incongruence and yield data is also described by Johnson (U.S. Pat. No. 5,492,547). These studies do not use markers as a selection tool, but instead attempt to use marker data to reduce the amount of costly yield trialing.